Overview

Dataset statistics

Number of variables22
Number of observations39607
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.6 MiB
Average record size in memory176.0 B

Variable types

Numeric20
Categorical2

Alerts

X_47 has constant value "1" Constant
X_48 has constant value "1" Constant
X_41 is highly correlated with X_43High correlation
X_42 is highly correlated with X_44 and 1 other fieldsHigh correlation
X_43 is highly correlated with X_41 and 1 other fieldsHigh correlation
X_44 is highly correlated with X_42High correlation
X_45 is highly correlated with X_42 and 1 other fieldsHigh correlation
Y_01 is highly correlated with Y_02 and 1 other fieldsHigh correlation
Y_02 is highly correlated with Y_01 and 1 other fieldsHigh correlation
Y_03 is highly correlated with Y_01 and 1 other fieldsHigh correlation
Y_04 is highly correlated with Y_05 and 4 other fieldsHigh correlation
Y_05 is highly correlated with Y_04 and 9 other fieldsHigh correlation
Y_06 is highly correlated with Y_04 and 9 other fieldsHigh correlation
Y_07 is highly correlated with Y_04 and 4 other fieldsHigh correlation
Y_08 is highly correlated with Y_05 and 7 other fieldsHigh correlation
Y_09 is highly correlated with Y_05 and 7 other fieldsHigh correlation
Y_10 is highly correlated with Y_04 and 9 other fieldsHigh correlation
Y_11 is highly correlated with Y_04 and 9 other fieldsHigh correlation
Y_12 is highly correlated with Y_05 and 7 other fieldsHigh correlation
Y_13 is highly correlated with Y_05 and 7 other fieldsHigh correlation
Y_14 is highly correlated with Y_05 and 7 other fieldsHigh correlation
X_41 is highly correlated with X_43High correlation
X_42 is highly correlated with X_44 and 1 other fieldsHigh correlation
X_43 is highly correlated with X_41 and 1 other fieldsHigh correlation
X_44 is highly correlated with X_42High correlation
X_45 is highly correlated with X_42 and 1 other fieldsHigh correlation
Y_01 is highly correlated with Y_02 and 1 other fieldsHigh correlation
Y_02 is highly correlated with Y_01 and 1 other fieldsHigh correlation
Y_03 is highly correlated with Y_01 and 1 other fieldsHigh correlation
Y_04 is highly correlated with Y_05 and 8 other fieldsHigh correlation
Y_05 is highly correlated with Y_04 and 8 other fieldsHigh correlation
Y_06 is highly correlated with Y_10High correlation
Y_07 is highly correlated with Y_04 and 2 other fieldsHigh correlation
Y_08 is highly correlated with Y_04 and 7 other fieldsHigh correlation
Y_09 is highly correlated with Y_04 and 7 other fieldsHigh correlation
Y_10 is highly correlated with Y_04 and 8 other fieldsHigh correlation
Y_11 is highly correlated with Y_04 and 8 other fieldsHigh correlation
Y_12 is highly correlated with Y_04 and 7 other fieldsHigh correlation
Y_13 is highly correlated with Y_04 and 7 other fieldsHigh correlation
Y_14 is highly correlated with Y_04 and 7 other fieldsHigh correlation
X_41 is highly correlated with X_43High correlation
X_42 is highly correlated with X_44High correlation
X_43 is highly correlated with X_41 and 1 other fieldsHigh correlation
X_44 is highly correlated with X_42High correlation
X_45 is highly correlated with X_43High correlation
Y_01 is highly correlated with Y_02 and 1 other fieldsHigh correlation
Y_02 is highly correlated with Y_01 and 1 other fieldsHigh correlation
Y_03 is highly correlated with Y_01 and 1 other fieldsHigh correlation
Y_04 is highly correlated with Y_05High correlation
Y_05 is highly correlated with Y_04High correlation
Y_06 is highly correlated with Y_08 and 6 other fieldsHigh correlation
Y_08 is highly correlated with Y_06 and 6 other fieldsHigh correlation
Y_09 is highly correlated with Y_06 and 6 other fieldsHigh correlation
Y_10 is highly correlated with Y_06 and 6 other fieldsHigh correlation
Y_11 is highly correlated with Y_06 and 6 other fieldsHigh correlation
Y_12 is highly correlated with Y_06 and 6 other fieldsHigh correlation
Y_13 is highly correlated with Y_06 and 6 other fieldsHigh correlation
Y_14 is highly correlated with Y_06 and 6 other fieldsHigh correlation
X_47 is highly correlated with X_48High correlation
X_48 is highly correlated with X_47High correlation
X_41 is highly correlated with X_42 and 3 other fieldsHigh correlation
X_42 is highly correlated with X_41 and 2 other fieldsHigh correlation
X_43 is highly correlated with X_41 and 2 other fieldsHigh correlation
X_44 is highly correlated with X_41 and 2 other fieldsHigh correlation
X_45 is highly correlated with X_41 and 2 other fieldsHigh correlation
Y_01 is highly correlated with Y_02 and 4 other fieldsHigh correlation
Y_02 is highly correlated with Y_01 and 1 other fieldsHigh correlation
Y_03 is highly correlated with Y_01 and 1 other fieldsHigh correlation
Y_04 is highly correlated with Y_05 and 7 other fieldsHigh correlation
Y_05 is highly correlated with Y_04 and 8 other fieldsHigh correlation
Y_06 is highly correlated with Y_01 and 8 other fieldsHigh correlation
Y_07 is highly correlated with Y_01 and 4 other fieldsHigh correlation
Y_08 is highly correlated with Y_04 and 8 other fieldsHigh correlation
Y_09 is highly correlated with Y_04 and 8 other fieldsHigh correlation
Y_10 is highly correlated with Y_01 and 10 other fieldsHigh correlation
Y_11 is highly correlated with Y_04 and 9 other fieldsHigh correlation
Y_12 is highly correlated with Y_04 and 8 other fieldsHigh correlation
Y_13 is highly correlated with Y_04 and 8 other fieldsHigh correlation
Y_14 is highly correlated with Y_04 and 8 other fieldsHigh correlation

Reproduction

Analysis started2022-08-06 10:07:29.284439
Analysis finished2022-08-06 10:08:27.777834
Duration58.49 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

X_41
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct39
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.18699169
Minimum20.73
Maximum21.62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T19:08:27.876936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum20.73
5-th percentile21.14
Q121.17
median21.19
Q321.21
95-th percentile21.24
Maximum21.62
Range0.89
Interquartile range (IQR)0.04

Descriptive statistics

Standard deviation0.03112772914
Coefficient of variation (CV)0.001469190605
Kurtosis4.995347826
Mean21.18699169
Median Absolute Deviation (MAD)0.02
Skewness-0.1304756492
Sum839153.18
Variance0.0009689355216
MonotonicityNot monotonic
2022-08-06T19:08:28.000180image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
21.195607
14.2%
21.175156
13.0%
21.184792
12.1%
21.24205
10.6%
21.214133
10.4%
21.163314
8.4%
21.152764
7.0%
21.222500
6.3%
21.231898
 
4.8%
21.141416
 
3.6%
Other values (29)3822
9.6%
ValueCountFrequency (%)
20.731
 
< 0.1%
20.781
 
< 0.1%
20.811
 
< 0.1%
20.852
 
< 0.1%
20.861
 
< 0.1%
20.981
 
< 0.1%
21.011
 
< 0.1%
21.021
 
< 0.1%
21.053
 
< 0.1%
21.0612
< 0.1%
ValueCountFrequency (%)
21.621
 
< 0.1%
21.511
 
< 0.1%
21.332
 
< 0.1%
21.321
 
< 0.1%
21.3112
 
< 0.1%
21.319
 
< 0.1%
21.2928
 
0.1%
21.2888
 
0.2%
21.27139
0.4%
21.26346
0.9%

X_42
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct42
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.05933421
Minimum20.79
Maximum21.44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T19:08:28.140774image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum20.79
5-th percentile21
Q121.03
median21.06
Q321.09
95-th percentile21.13
Maximum21.44
Range0.65
Interquartile range (IQR)0.06

Descriptive statistics

Standard deviation0.04028810521
Coefficient of variation (CV)0.001913075922
Kurtosis0.6365996718
Mean21.05933421
Median Absolute Deviation (MAD)0.03
Skewness0.05154079206
Sum834097.05
Variance0.001623131422
MonotonicityNot monotonic
2022-08-06T19:08:28.281363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
21.064285
10.8%
21.053779
9.5%
21.083773
9.5%
21.043577
9.0%
21.073345
8.4%
21.033223
 
8.1%
21.092559
 
6.5%
21.12549
 
6.4%
21.022403
 
6.1%
21.012041
 
5.2%
Other values (32)8073
20.4%
ValueCountFrequency (%)
20.791
 
< 0.1%
20.811
 
< 0.1%
20.896
 
< 0.1%
20.99
 
< 0.1%
20.9113
 
< 0.1%
20.9228
 
0.1%
20.9330
 
0.1%
20.9461
0.2%
20.95101
0.3%
20.96147
0.4%
ValueCountFrequency (%)
21.441
 
< 0.1%
21.312
 
< 0.1%
21.283
 
< 0.1%
21.252
 
< 0.1%
21.244
 
< 0.1%
21.233
 
< 0.1%
21.223
 
< 0.1%
21.214
 
< 0.1%
21.25
 
< 0.1%
21.1933
0.1%

X_43
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct50
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.2037049
Minimum20.8
Maximum21.41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T19:08:28.908228image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum20.8
5-th percentile21.13
Q121.17
median21.2
Q321.24
95-th percentile21.28
Maximum21.41
Range0.61
Interquartile range (IQR)0.07

Descriptive statistics

Standard deviation0.0472109947
Coefficient of variation (CV)0.002226544602
Kurtosis0.7139276034
Mean21.2037049
Median Absolute Deviation (MAD)0.03
Skewness-0.1518542186
Sum839815.14
Variance0.00222887802
MonotonicityNot monotonic
2022-08-06T19:08:29.066811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.193832
 
9.7%
21.213753
 
9.5%
21.23165
 
8.0%
21.172932
 
7.4%
21.182832
 
7.2%
21.232812
 
7.1%
21.222799
 
7.1%
21.242646
 
6.7%
21.161939
 
4.9%
21.251876
 
4.7%
Other values (40)11021
27.8%
ValueCountFrequency (%)
20.81
 
< 0.1%
20.841
 
< 0.1%
20.922
< 0.1%
20.931
 
< 0.1%
20.951
 
< 0.1%
20.971
 
< 0.1%
20.981
 
< 0.1%
20.992
< 0.1%
213
< 0.1%
21.014
< 0.1%
ValueCountFrequency (%)
21.411
 
< 0.1%
21.41
 
< 0.1%
21.394
 
< 0.1%
21.384
 
< 0.1%
21.378
 
< 0.1%
21.3612
 
< 0.1%
21.3526
 
0.1%
21.3446
 
0.1%
21.3381
0.2%
21.32156
0.4%

X_44
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct36
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.16010655
Minimum20.93
Maximum21.32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T19:08:29.223027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum20.93
5-th percentile21.1
Q121.13
median21.16
Q321.19
95-th percentile21.22
Maximum21.32
Range0.39
Interquartile range (IQR)0.06

Descriptive statistics

Standard deviation0.04217564213
Coefficient of variation (CV)0.001993167758
Kurtosis-0.08713690344
Mean21.16010655
Median Absolute Deviation (MAD)0.03
Skewness-0.3670477548
Sum838088.34
Variance0.001778784789
MonotonicityNot monotonic
2022-08-06T19:08:29.347993image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
21.194048
10.2%
21.143640
9.2%
21.123617
9.1%
21.133468
8.8%
21.23329
8.4%
21.213305
8.3%
21.152986
 
7.5%
21.182736
 
6.9%
21.172259
 
5.7%
21.111978
 
5.0%
Other values (26)8241
20.8%
ValueCountFrequency (%)
20.931
 
< 0.1%
20.941
 
< 0.1%
20.951
 
< 0.1%
20.965
 
< 0.1%
20.984
 
< 0.1%
20.999
 
< 0.1%
2115
 
< 0.1%
21.0148
0.1%
21.0255
0.1%
21.03115
0.3%
ValueCountFrequency (%)
21.321
 
< 0.1%
21.281
 
< 0.1%
21.271
 
< 0.1%
21.2612
 
< 0.1%
21.2567
 
0.2%
21.24327
 
0.8%
21.23963
 
2.4%
21.221818
4.6%
21.213305
8.3%
21.23329
8.4%

X_45
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct39
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.154567122
Minimum0
Maximum0.42
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T19:08:29.504206image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.08
Q10.12
median0.15
Q30.19
95-th percentile0.23
Maximum0.42
Range0.42
Interquartile range (IQR)0.07

Descriptive statistics

Standard deviation0.04696751446
Coefficient of variation (CV)0.3038648444
Kurtosis-0.3297400879
Mean0.154567122
Median Absolute Deviation (MAD)0.03
Skewness0.2427426786
Sum6121.94
Variance0.002205947415
MonotonicityNot monotonic
2022-08-06T19:08:29.629176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0.133124
 
7.9%
0.122999
 
7.6%
0.142923
 
7.4%
0.162897
 
7.3%
0.152888
 
7.3%
0.172767
 
7.0%
0.112730
 
6.9%
0.182640
 
6.7%
0.192413
 
6.1%
0.12209
 
5.6%
Other values (29)12017
30.3%
ValueCountFrequency (%)
01
 
< 0.1%
0.012
 
< 0.1%
0.023
 
< 0.1%
0.0318
 
< 0.1%
0.0465
 
0.2%
0.05160
 
0.4%
0.06318
 
0.8%
0.07594
 
1.5%
0.081029
2.6%
0.091594
4.0%
ValueCountFrequency (%)
0.421
 
< 0.1%
0.392
 
< 0.1%
0.361
 
< 0.1%
0.353
 
< 0.1%
0.341
 
< 0.1%
0.334
 
< 0.1%
0.324
 
< 0.1%
0.3113
 
< 0.1%
0.327
0.1%
0.2957
0.1%

X_46
Real number (ℝ≥0)

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1468.275305
Minimum1457
Maximum1469
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T19:08:29.754147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1457
5-th percentile1463
Q11469
median1469
Q31469
95-th percentile1469
Maximum1469
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.121517174
Coefficient of variation (CV)0.001444904213
Kurtosis10.33249257
Mean1468.275305
Median Absolute Deviation (MAD)0
Skewness-3.246227076
Sum58153980
Variance4.500835118
MonotonicityNot monotonic
2022-08-06T19:08:29.865924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
146934088
86.1%
1465881
 
2.2%
1464780
 
2.0%
1468696
 
1.8%
1466610
 
1.5%
1463549
 
1.4%
1467389
 
1.0%
1462365
 
0.9%
1461308
 
0.8%
1460305
 
0.8%
Other values (3)636
 
1.6%
ValueCountFrequency (%)
1457180
 
0.5%
1458197
 
0.5%
1459259
 
0.7%
1460305
 
0.8%
1461308
 
0.8%
1462365
0.9%
1463549
1.4%
1464780
2.0%
1465881
2.2%
1466610
1.5%
ValueCountFrequency (%)
146934088
86.1%
1468696
 
1.8%
1467389
 
1.0%
1466610
 
1.5%
1465881
 
2.2%
1464780
 
2.0%
1463549
 
1.4%
1462365
 
0.9%
1461308
 
0.8%
1460305
 
0.8%

X_47
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size309.6 KiB
1
39607 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters39607
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
139607
100.0%

Length

2022-08-06T19:08:29.990926image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-06T19:08:30.100273image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
139607
100.0%

Most occurring characters

ValueCountFrequency (%)
139607
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number39607
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
139607
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common39607
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
139607
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII39607
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
139607
100.0%

X_48
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size309.6 KiB
1
39607 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters39607
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
139607
100.0%

Length

2022-08-06T19:08:30.194003image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-06T19:08:30.303351image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
139607
100.0%

Most occurring characters

ValueCountFrequency (%)
139607
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number39607
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
139607
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common39607
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
139607
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII39607
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
139607
100.0%

Y_01
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2249
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.353813796
Minimum0.017
Maximum4.409
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T19:08:30.428322image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.017
5-th percentile0.7833
Q11.1275
median1.349
Q31.576
95-th percentile1.931
Maximum4.409
Range4.392
Interquartile range (IQR)0.4485

Descriptive statistics

Standard deviation0.3562231101
Coefficient of variation (CV)0.2631256316
Kurtosis1.210970899
Mean1.353813796
Median Absolute Deviation (MAD)0.224
Skewness0.1502434869
Sum53620.503
Variance0.1268949041
MonotonicityNot monotonic
2022-08-06T19:08:30.568913image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.37664
 
0.2%
1.31262
 
0.2%
1.33360
 
0.2%
1.4260
 
0.2%
1.38960
 
0.2%
1.27860
 
0.2%
1.360
 
0.2%
1.30859
 
0.1%
1.458
 
0.1%
1.26358
 
0.1%
Other values (2239)39006
98.5%
ValueCountFrequency (%)
0.0171
 
< 0.1%
0.0181
 
< 0.1%
0.0192
< 0.1%
0.023
< 0.1%
0.0212
< 0.1%
0.0252
< 0.1%
0.0262
< 0.1%
0.0271
 
< 0.1%
0.0281
 
< 0.1%
0.0351
 
< 0.1%
ValueCountFrequency (%)
4.4091
< 0.1%
4.0811
< 0.1%
3.791
< 0.1%
3.721
< 0.1%
3.5291
< 0.1%
3.5181
< 0.1%
3.5011
< 0.1%
3.4991
< 0.1%
3.4191
< 0.1%
3.3641
< 0.1%

Y_02
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2227
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.057267251
Minimum0.007
Maximum3.998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T19:08:30.725132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.007
5-th percentile0.45
Q10.793
median1.044
Q31.3
95-th percentile1.711
Maximum3.998
Range3.991
Interquartile range (IQR)0.507

Descriptive statistics

Standard deviation0.386265985
Coefficient of variation (CV)0.3653437527
Kurtosis0.6736418075
Mean1.057267251
Median Absolute Deviation (MAD)0.254
Skewness0.3657652688
Sum41875.184
Variance0.1492014112
MonotonicityNot monotonic
2022-08-06T19:08:30.883420image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.07259
 
0.1%
0.81459
 
0.1%
1.1258
 
0.1%
1.04357
 
0.1%
0.92456
 
0.1%
0.88856
 
0.1%
1.03155
 
0.1%
1.14455
 
0.1%
0.90255
 
0.1%
0.83454
 
0.1%
Other values (2217)39043
98.6%
ValueCountFrequency (%)
0.0072
 
< 0.1%
0.0084
< 0.1%
0.0093
< 0.1%
0.014
< 0.1%
0.0113
< 0.1%
0.0123
< 0.1%
0.0135
< 0.1%
0.0142
 
< 0.1%
0.0157
< 0.1%
0.0166
< 0.1%
ValueCountFrequency (%)
3.9981
< 0.1%
3.971
< 0.1%
3.721
< 0.1%
3.5521
< 0.1%
3.2881
< 0.1%
3.2561
< 0.1%
3.2281
< 0.1%
3.1421
< 0.1%
3.1151
< 0.1%
3.0491
< 0.1%

Y_03
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2127
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.014001717
Minimum0.017
Maximum3.756
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T19:08:31.039634image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.017
5-th percentile0.451
Q10.769
median0.998
Q31.239
95-th percentile1.628
Maximum3.756
Range3.739
Interquartile range (IQR)0.47

Descriptive statistics

Standard deviation0.3614919509
Coefficient of variation (CV)0.3565003341
Kurtosis0.7764764849
Mean1.014001717
Median Absolute Deviation (MAD)0.235
Skewness0.396399124
Sum40161.566
Variance0.1306764305
MonotonicityNot monotonic
2022-08-06T19:08:31.195847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.88866
 
0.2%
0.98863
 
0.2%
0.97362
 
0.2%
0.97161
 
0.2%
0.96561
 
0.2%
1.10460
 
0.2%
0.86960
 
0.2%
0.99959
 
0.1%
0.90657
 
0.1%
0.84657
 
0.1%
Other values (2117)39001
98.5%
ValueCountFrequency (%)
0.0171
 
< 0.1%
0.0191
 
< 0.1%
0.0214
< 0.1%
0.0221
 
< 0.1%
0.0242
< 0.1%
0.0252
< 0.1%
0.0272
< 0.1%
0.0293
< 0.1%
0.032
< 0.1%
0.0311
 
< 0.1%
ValueCountFrequency (%)
3.7561
< 0.1%
3.7131
< 0.1%
3.2841
< 0.1%
3.2131
< 0.1%
3.1981
< 0.1%
3.1821
< 0.1%
3.1021
< 0.1%
3.0991
< 0.1%
3.0691
< 0.1%
3.0281
< 0.1%

Y_04
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10773
Distinct (%)27.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.62119133
Minimum-0.331
Maximum98.794
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)< 0.1%
Memory size309.6 KiB
2022-08-06T19:08:31.352055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.331
5-th percentile8.9393
Q111.822
median13.837
Q315.626
95-th percentile17.5587
Maximum98.794
Range99.125
Interquartile range (IQR)3.804

Descriptive statistics

Standard deviation2.686631665
Coefficient of variation (CV)0.1972391107
Kurtosis25.18483477
Mean13.62119133
Median Absolute Deviation (MAD)1.887
Skewness0.4534505598
Sum539494.525
Variance7.217989702
MonotonicityNot monotonic
2022-08-06T19:08:31.492651image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.85215
 
< 0.1%
14.24314
 
< 0.1%
13.34914
 
< 0.1%
15.70713
 
< 0.1%
15.46113
 
< 0.1%
13.8913
 
< 0.1%
14.40913
 
< 0.1%
14.77413
 
< 0.1%
15.06313
 
< 0.1%
15.43613
 
< 0.1%
Other values (10763)39473
99.7%
ValueCountFrequency (%)
-0.3311
< 0.1%
-0.3271
< 0.1%
-0.3081
< 0.1%
2.2421
< 0.1%
3.3121
< 0.1%
3.4471
< 0.1%
3.4781
< 0.1%
3.8331
< 0.1%
3.8471
< 0.1%
3.9241
< 0.1%
ValueCountFrequency (%)
98.7941
< 0.1%
33.3331
< 0.1%
25.9561
< 0.1%
21.4621
< 0.1%
21.4421
< 0.1%
20.891
< 0.1%
20.4761
< 0.1%
20.3211
< 0.1%
20.2091
< 0.1%
20.2041
< 0.1%

Y_05
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10241
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.29046706
Minimum18.589
Maximum37.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T19:08:31.648871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum18.589
5-th percentile26.512
Q129.768
median31.71
Q333.184
95-th percentile34.709
Maximum37.25
Range18.661
Interquartile range (IQR)3.416

Descriptive statistics

Standard deviation2.543221628
Coefficient of variation (CV)0.08127784168
Kurtosis0.489914823
Mean31.29046706
Median Absolute Deviation (MAD)1.658
Skewness-0.7720326285
Sum1239321.529
Variance6.467976249
MonotonicityNot monotonic
2022-08-06T19:08:31.789427image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32.69218
 
< 0.1%
33.46517
 
< 0.1%
32.49116
 
< 0.1%
31.94915
 
< 0.1%
33.21515
 
< 0.1%
31.71315
 
< 0.1%
32.65915
 
< 0.1%
32.59315
 
< 0.1%
32.715
 
< 0.1%
32.79615
 
< 0.1%
Other values (10231)39451
99.6%
ValueCountFrequency (%)
18.5891
< 0.1%
19.3951
< 0.1%
19.7041
< 0.1%
20.0621
< 0.1%
20.0672
< 0.1%
20.1231
< 0.1%
20.1891
< 0.1%
20.241
< 0.1%
20.4171
< 0.1%
20.4621
< 0.1%
ValueCountFrequency (%)
37.251
< 0.1%
37.2251
< 0.1%
37.1012
< 0.1%
36.9951
< 0.1%
36.9791
< 0.1%
36.9151
< 0.1%
36.8681
< 0.1%
36.8371
< 0.1%
36.8081
< 0.1%
36.8061
< 0.1%

Y_06
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4269
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.52938208
Minimum-19.963
Maximum18.998
Zeros0
Zeros (%)0.0%
Negative99
Negative (%)0.2%
Memory size309.6 KiB
2022-08-06T19:08:31.963164image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-19.963
5-th percentile15.177
Q116.146
median16.694
Q317.164
95-th percentile17.758
Maximum18.998
Range38.961
Interquartile range (IQR)1.018

Descriptive statistics

Standard deviation1.89301384
Coefficient of variation (CV)0.1145241747
Kurtosis270.339787
Mean16.52938208
Median Absolute Deviation (MAD)0.501
Skewness-15.02970347
Sum654679.236
Variance3.583501399
MonotonicityNot monotonic
2022-08-06T19:08:32.119376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.78238
 
0.1%
16.87236
 
0.1%
17.09933
 
0.1%
16.85932
 
0.1%
17.13832
 
0.1%
16.96732
 
0.1%
16.7232
 
0.1%
16.84731
 
0.1%
16.7631
 
0.1%
16.47531
 
0.1%
Other values (4259)39279
99.2%
ValueCountFrequency (%)
-19.9631
< 0.1%
-19.6021
< 0.1%
-19.5171
< 0.1%
-19.4721
< 0.1%
-19.4431
< 0.1%
-19.3671
< 0.1%
-19.3511
< 0.1%
-19.2521
< 0.1%
-19.232
< 0.1%
-19.0991
< 0.1%
ValueCountFrequency (%)
18.9981
< 0.1%
18.9921
< 0.1%
18.8881
< 0.1%
18.8571
< 0.1%
18.8241
< 0.1%
18.7861
< 0.1%
18.7531
< 0.1%
18.7281
< 0.1%
18.6921
< 0.1%
18.6851
< 0.1%

Y_07
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2394
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.155054107
Minimum0.502
Maximum5.299
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T19:08:32.275590image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.502
5-th percentile2.53
Q12.863
median3.126
Q33.4335
95-th percentile3.864
Maximum5.299
Range4.797
Interquartile range (IQR)0.5705

Descriptive statistics

Standard deviation0.4189399013
Coefficient of variation (CV)0.1327837454
Kurtosis0.7671083874
Mean3.155054107
Median Absolute Deviation (MAD)0.283
Skewness0.08450194006
Sum124962.228
Variance0.1755106409
MonotonicityNot monotonic
2022-08-06T19:08:32.431798image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.00557
 
0.1%
2.9456
 
0.1%
3.13653
 
0.1%
2.98852
 
0.1%
3.12852
 
0.1%
3.03351
 
0.1%
3.27251
 
0.1%
2.97651
 
0.1%
3.0351
 
0.1%
3.0850
 
0.1%
Other values (2384)39083
98.7%
ValueCountFrequency (%)
0.5021
< 0.1%
0.6851
< 0.1%
0.7231
< 0.1%
0.8181
< 0.1%
0.8791
< 0.1%
0.9111
< 0.1%
0.9211
< 0.1%
0.9331
< 0.1%
0.9451
< 0.1%
0.9531
< 0.1%
ValueCountFrequency (%)
5.2991
< 0.1%
5.1181
< 0.1%
4.9991
< 0.1%
4.9911
< 0.1%
4.9821
< 0.1%
4.9271
< 0.1%
4.9181
< 0.1%
4.911
< 0.1%
4.8681
< 0.1%
4.851
< 0.1%

Y_08
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3672
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-26.29483879
Minimum-29.652
Maximum-23.785
Zeros0
Zeros (%)0.0%
Negative39607
Negative (%)100.0%
Memory size309.6 KiB
2022-08-06T19:08:32.572395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-29.652
5-th percentile-27.4447
Q1-26.689
median-26.254
Q3-25.855
95-th percentile-25.283
Maximum-23.785
Range5.867
Interquartile range (IQR)0.834

Descriptive statistics

Standard deviation0.6605368289
Coefficient of variation (CV)-0.0251203985
Kurtosis0.7493218708
Mean-26.29483879
Median Absolute Deviation (MAD)0.415
Skewness-0.4373902743
Sum-1041459.68
Variance0.4363089024
MonotonicityNot monotonic
2022-08-06T19:08:32.728609image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-26.31441
 
0.1%
-26.09140
 
0.1%
-25.83840
 
0.1%
-26.43139
 
0.1%
-26.43539
 
0.1%
-26.0939
 
0.1%
-26.08138
 
0.1%
-26.37238
 
0.1%
-26.04537
 
0.1%
-26.12236
 
0.1%
Other values (3662)39220
99.0%
ValueCountFrequency (%)
-29.6521
< 0.1%
-29.6421
< 0.1%
-29.6051
< 0.1%
-29.5781
< 0.1%
-29.4521
< 0.1%
-29.3521
< 0.1%
-29.331
< 0.1%
-29.3241
< 0.1%
-29.3092
< 0.1%
-29.3061
< 0.1%
ValueCountFrequency (%)
-23.7851
< 0.1%
-24.0131
< 0.1%
-24.1171
< 0.1%
-24.1421
< 0.1%
-24.1581
< 0.1%
-24.1621
< 0.1%
-24.181
< 0.1%
-24.191
< 0.1%
-24.2071
< 0.1%
-24.2111
< 0.1%

Y_09
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3649
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-26.30862254
Minimum-29.523
Maximum-23.96
Zeros0
Zeros (%)0.0%
Negative39607
Negative (%)100.0%
Memory size309.6 KiB
2022-08-06T19:08:32.871319image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-29.523
5-th percentile-27.44
Q1-26.702
median-26.266
Q3-25.871
95-th percentile-25.311
Maximum-23.96
Range5.563
Interquartile range (IQR)0.831

Descriptive statistics

Standard deviation0.6535798156
Coefficient of variation (CV)-0.02484279877
Kurtosis0.7264309573
Mean-26.30862254
Median Absolute Deviation (MAD)0.414
Skewness-0.4318247115
Sum-1042005.613
Variance0.4271665753
MonotonicityNot monotonic
2022-08-06T19:08:33.011908image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-26.3143
 
0.1%
-26.22841
 
0.1%
-26.2838
 
0.1%
-26.10638
 
0.1%
-26.26537
 
0.1%
-26.12337
 
0.1%
-26.0437
 
0.1%
-26.37537
 
0.1%
-26.1137
 
0.1%
-26.33136
 
0.1%
Other values (3639)39226
99.0%
ValueCountFrequency (%)
-29.5231
< 0.1%
-29.4771
< 0.1%
-29.471
< 0.1%
-29.4271
< 0.1%
-29.3921
< 0.1%
-29.3761
< 0.1%
-29.3511
< 0.1%
-29.3391
< 0.1%
-29.3381
< 0.1%
-29.3311
< 0.1%
ValueCountFrequency (%)
-23.961
< 0.1%
-23.9851
< 0.1%
-24.0911
< 0.1%
-24.1041
< 0.1%
-24.1551
< 0.1%
-24.161
< 0.1%
-24.1891
< 0.1%
-24.2191
< 0.1%
-24.2421
< 0.1%
-24.2761
< 0.1%

Y_10
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4458
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-22.40006244
Minimum-31.119
Maximum-20.052
Zeros0
Zeros (%)0.0%
Negative39607
Negative (%)100.0%
Memory size309.6 KiB
2022-08-06T19:08:33.183736image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-31.119
5-th percentile-23.9517
Q1-22.871
median-22.275
Q3-21.791
95-th percentile-21.195
Maximum-20.052
Range11.067
Interquartile range (IQR)1.08

Descriptive statistics

Standard deviation0.920952195
Coefficient of variation (CV)-0.04111382268
Kurtosis10.34745855
Mean-22.40006244
Median Absolute Deviation (MAD)0.529
Skewness-1.837054602
Sum-887199.273
Variance0.8481529455
MonotonicityNot monotonic
2022-08-06T19:08:33.319220image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-22.01433
 
0.1%
-21.99533
 
0.1%
-21.91932
 
0.1%
-22.09632
 
0.1%
-22.32732
 
0.1%
-22.17631
 
0.1%
-22.09231
 
0.1%
-22.32931
 
0.1%
-22.06531
 
0.1%
-21.78630
 
0.1%
Other values (4448)39291
99.2%
ValueCountFrequency (%)
-31.1191
< 0.1%
-30.9491
< 0.1%
-30.9261
< 0.1%
-30.7881
< 0.1%
-30.6191
< 0.1%
-30.5871
< 0.1%
-30.5841
< 0.1%
-30.5481
< 0.1%
-30.5371
< 0.1%
-30.5071
< 0.1%
ValueCountFrequency (%)
-20.0521
 
< 0.1%
-20.0931
 
< 0.1%
-20.131
 
< 0.1%
-20.1471
 
< 0.1%
-20.2241
 
< 0.1%
-20.2351
 
< 0.1%
-20.2721
 
< 0.1%
-20.2883
< 0.1%
-20.311
 
< 0.1%
-20.3311
 
< 0.1%

Y_11
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4309
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.32506113
Minimum19.844
Maximum26.703
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T19:08:33.475431image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum19.844
5-th percentile22.815
Q123.836
median24.42
Q324.9115
95-th percentile25.514
Maximum26.703
Range6.859
Interquartile range (IQR)1.0755

Descriptive statistics

Standard deviation0.8301968024
Coefficient of variation (CV)0.03412927919
Kurtosis0.7579205164
Mean24.32506113
Median Absolute Deviation (MAD)0.532
Skewness-0.6749349242
Sum963442.696
Variance0.6892267307
MonotonicityNot monotonic
2022-08-06T19:08:33.616023image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.73734
 
0.1%
24.77634
 
0.1%
24.49633
 
0.1%
24.5832
 
0.1%
24.54232
 
0.1%
24.40932
 
0.1%
24.64432
 
0.1%
24.50931
 
0.1%
24.74131
 
0.1%
24.58831
 
0.1%
Other values (4299)39285
99.2%
ValueCountFrequency (%)
19.8441
< 0.1%
20.0311
< 0.1%
20.0451
< 0.1%
20.1011
< 0.1%
20.1751
< 0.1%
20.1941
< 0.1%
20.1991
< 0.1%
20.2951
< 0.1%
20.2981
< 0.1%
20.3341
< 0.1%
ValueCountFrequency (%)
26.7031
< 0.1%
26.6591
< 0.1%
26.6571
< 0.1%
26.5921
< 0.1%
26.5791
< 0.1%
26.5671
< 0.1%
26.5511
< 0.1%
26.5451
< 0.1%
26.4831
< 0.1%
26.481
< 0.1%

Y_12
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3673
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-26.23776173
Minimum-29.544
Maximum-23.722
Zeros0
Zeros (%)0.0%
Negative39607
Negative (%)100.0%
Memory size309.6 KiB
2022-08-06T19:08:33.772236image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-29.544
5-th percentile-27.38
Q1-26.63
median-26.198
Q3-25.799
95-th percentile-25.238
Maximum-23.722
Range5.822
Interquartile range (IQR)0.831

Descriptive statistics

Standard deviation0.6563285123
Coefficient of variation (CV)-0.02501465327
Kurtosis0.7459825
Mean-26.23776173
Median Absolute Deviation (MAD)0.413
Skewness-0.4446574078
Sum-1039199.029
Variance0.430767116
MonotonicityNot monotonic
2022-08-06T19:08:33.914969image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-26.02649
 
0.1%
-26.1741
 
0.1%
-26.07640
 
0.1%
-26.35839
 
0.1%
-26.11839
 
0.1%
-26.4438
 
0.1%
-26.29238
 
0.1%
-25.99837
 
0.1%
-25.91937
 
0.1%
-26.35437
 
0.1%
Other values (3663)39212
99.0%
ValueCountFrequency (%)
-29.5441
< 0.1%
-29.4531
< 0.1%
-29.4411
< 0.1%
-29.3671
< 0.1%
-29.3461
< 0.1%
-29.3411
< 0.1%
-29.3351
< 0.1%
-29.311
< 0.1%
-29.2871
< 0.1%
-29.2831
< 0.1%
ValueCountFrequency (%)
-23.7221
< 0.1%
-23.9471
< 0.1%
-23.951
< 0.1%
-24.0671
< 0.1%
-24.1511
< 0.1%
-24.161
< 0.1%
-24.2211
< 0.1%
-24.2281
< 0.1%
-24.2311
< 0.1%
-24.241
< 0.1%

Y_13
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3665
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-26.23386934
Minimum-29.448
Maximum-23.899
Zeros0
Zeros (%)0.0%
Negative39607
Negative (%)100.0%
Memory size309.6 KiB
2022-08-06T19:08:34.071180image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-29.448
5-th percentile-27.366
Q1-26.624
median-26.193
Q3-25.794
95-th percentile-25.2393
Maximum-23.899
Range5.549
Interquartile range (IQR)0.83

Descriptive statistics

Standard deviation0.6550900257
Coefficient of variation (CV)-0.02497115531
Kurtosis0.7518019689
Mean-26.23386934
Median Absolute Deviation (MAD)0.413
Skewness-0.4398630698
Sum-1039044.863
Variance0.4291429417
MonotonicityNot monotonic
2022-08-06T19:08:34.211776image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-26.09742
 
0.1%
-26.18741
 
0.1%
-26.33641
 
0.1%
-26.2840
 
0.1%
-26.15139
 
0.1%
-26.34638
 
0.1%
-26.03938
 
0.1%
-25.96837
 
0.1%
-26.21336
 
0.1%
-25.99536
 
0.1%
Other values (3655)39219
99.0%
ValueCountFrequency (%)
-29.4481
< 0.1%
-29.4431
< 0.1%
-29.3751
< 0.1%
-29.3681
< 0.1%
-29.3551
< 0.1%
-29.351
< 0.1%
-29.3011
< 0.1%
-29.2921
< 0.1%
-29.2361
< 0.1%
-29.2261
< 0.1%
ValueCountFrequency (%)
-23.8991
< 0.1%
-23.9361
< 0.1%
-23.9651
< 0.1%
-24.0211
< 0.1%
-24.1171
< 0.1%
-24.1231
< 0.1%
-24.1771
< 0.1%
-24.1941
< 0.1%
-24.2051
< 0.1%
-24.211
< 0.1%

Y_14
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3682
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-26.24586843
Minimum-29.62
Maximum-23.856
Zeros0
Zeros (%)0.0%
Negative39607
Negative (%)100.0%
Memory size309.6 KiB
2022-08-06T19:08:34.367987image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-29.62
5-th percentile-27.3817
Q1-26.64
median-26.204
Q3-25.809
95-th percentile-25.245
Maximum-23.856
Range5.764
Interquartile range (IQR)0.831

Descriptive statistics

Standard deviation0.6559887312
Coefficient of variation (CV)-0.02499398078
Kurtosis0.734812393
Mean-26.24586843
Median Absolute Deviation (MAD)0.413
Skewness-0.4307872388
Sum-1039520.111
Variance0.4303212155
MonotonicityNot monotonic
2022-08-06T19:08:34.508579image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-26.30346
 
0.1%
-26.02541
 
0.1%
-26.14839
 
0.1%
-25.85839
 
0.1%
-26.0839
 
0.1%
-26.17438
 
0.1%
-26.10538
 
0.1%
-25.83538
 
0.1%
-26.42437
 
0.1%
-26.06537
 
0.1%
Other values (3672)39215
99.0%
ValueCountFrequency (%)
-29.621
< 0.1%
-29.5291
< 0.1%
-29.4931
< 0.1%
-29.4341
< 0.1%
-29.341
< 0.1%
-29.3351
< 0.1%
-29.3121
< 0.1%
-29.2921
< 0.1%
-29.2821
< 0.1%
-29.281
< 0.1%
ValueCountFrequency (%)
-23.8561
< 0.1%
-24.0521
< 0.1%
-24.1372
< 0.1%
-24.1391
< 0.1%
-24.1651
< 0.1%
-24.1761
< 0.1%
-24.1921
< 0.1%
-24.1931
< 0.1%
-24.2081
< 0.1%
-24.2111
< 0.1%

Interactions

2022-08-06T19:08:24.430170image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:32.760676image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:35.260689image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:37.858305image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:40.470075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:43.166933image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:45.669479image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:48.230186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:50.699262image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:53.433581image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:57.531591image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:00.215117image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:03.043130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:05.869335image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:08.415654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:11.392459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:13.990285image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:16.570205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:19.232475image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:21.831888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:24.542636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:32.880711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:35.388308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:37.983615image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:40.600881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:43.274064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:45.781377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:48.343632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:50.828535image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:53.551545image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:57.660576image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:00.344296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:03.185635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:05.989367image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:08.534146image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:11.519250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:14.118509image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:16.694854image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:19.360514image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:21.942913image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:24.685521image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:33.005682image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:35.515849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:38.115133image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:40.737700image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:43.400718image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:45.930066image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:48.469600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:50.971964image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:53.696167image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:57.804541image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:00.495191image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:03.335656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:06.116154image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:08.668965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:11.655250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:14.246897image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:16.838668image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:19.489750image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:22.082207image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:24.809683image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:33.136454image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:35.652132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:38.243813image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:40.882667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:43.536275image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:46.057877image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:48.601115image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:51.119709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:53.834856image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:57.934577image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:00.639973image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:03.462706image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:06.242837image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:08.795565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:11.780310image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:14.389157image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:16.963423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:19.615677image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:22.217374image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:24.953513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:33.271228image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:35.778722image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:38.387332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:41.012595image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:43.663815image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:46.184396image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:48.727742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:51.261755image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:53.980385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:58.070143image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:00.786301image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:03.605326image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:06.390938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:08.937916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:11.922729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:14.515356image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:17.105780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:19.761060image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:22.350110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:25.064606image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:33.383258image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:35.907127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:38.514008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:41.140179image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:43.783621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:46.309366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:48.856253image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:51.393240image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:54.104391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:58.194769image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:00.916853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:03.747776image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:06.517718image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:09.062238image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:12.049474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:14.641841image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:17.233958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:19.888678image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:22.476869image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:25.207446image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:33.510740image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:36.033699image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:38.641761image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-08-06T19:07:45.157234image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:47.724948image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:50.203245image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:52.877159image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:56.966106image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:59.680703image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:02.469909image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:05.306760image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:07.903304image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:10.496045image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:13.483338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:16.057326image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:18.706810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:21.307996image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:23.911079image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:26.622367image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:34.885015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:37.461873image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:40.081350image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:42.762851image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:45.287172image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:47.854774image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:50.340987image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:53.022403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:57.114236image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:59.824471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:02.610498image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:05.458781image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:08.032424image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:10.622778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:13.610682image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:16.191575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:18.833645image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:21.442131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:24.039781image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:26.750253image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:35.012665image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:37.588541image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:40.210758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:42.890580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:45.415205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:47.977208image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:50.464834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:53.162188image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:57.259133image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:59.953778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:02.757244image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:05.585509image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:08.158093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:10.746846image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:13.733597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:16.316672image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:18.959616image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:21.572141image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:24.170665image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:26.883714image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:35.134107image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:37.726142image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:40.342554image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:43.022893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:45.540219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:48.103914image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:50.578052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:53.292399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:07:57.386904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:00.081480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:02.898050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:05.730291image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:08.284887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:11.265785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:13.867162image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:16.443428image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:19.090107image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:21.701302image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:08:24.301443image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-08-06T19:08:34.664793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-08-06T19:08:34.916837image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-08-06T19:08:35.166778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-08-06T19:08:35.385476image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-08-06T19:08:35.510452image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-08-06T19:08:27.104245image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-08-06T19:08:27.574706image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

X_41X_42X_43X_44X_45X_46X_47X_48Y_01Y_02Y_03Y_04Y_05Y_06Y_07Y_08Y_09Y_10Y_11Y_12Y_13Y_14
021.2020.9921.2821.090.291463112.0561.4561.68010.50229.63216.0834.276-25.381-25.529-22.76923.792-25.470-25.409-25.304
121.1621.0321.1621.130.131463111.4461.1841.26818.50733.17916.7363.229-26.619-26.523-22.57424.691-26.253-26.497-26.438
221.1321.0321.1721.120.141468111.2510.6650.78214.08231.80117.0802.839-26.238-26.216-22.16924.649-26.285-26.215-26.370
321.1820.9821.2021.090.221469111.4641.0791.05216.97534.50317.1433.144-25.426-25.079-21.76524.913-25.254-25.021-25.345
421.1620.9621.1821.100.221469110.9830.6460.68915.04732.60217.5693.138-25.376-25.242-21.07225.299-25.072-25.195-24.974
521.1721.0721.1921.160.121469111.1550.6780.58011.76029.66216.2013.343-26.466-26.527-22.62124.064-26.489-26.536-26.426
621.1820.9921.2021.100.211469112.1401.4381.68914.13732.73915.5732.418-27.581-28.038-23.35523.051-27.650-27.709-27.599
721.1921.0021.2321.120.231469111.7691.5351.53417.95434.68018.2303.147-24.917-24.832-20.68926.138-24.539-24.538-24.668
821.1721.0721.1721.160.101469111.3260.9450.88313.95229.12916.6083.931-25.890-25.801-22.52124.353-25.738-25.825-25.764
921.1321.0021.1721.100.171469112.0041.7871.54816.88534.20918.1202.646-25.520-25.408-21.15925.961-25.353-25.567-25.470

Last rows

X_41X_42X_43X_44X_45X_46X_47X_48Y_01Y_02Y_03Y_04Y_05Y_06Y_07Y_08Y_09Y_10Y_11Y_12Y_13Y_14
3959721.1521.0821.1921.130.111469111.4891.3691.30315.68734.08917.5863.107-25.927-25.836-21.61125.399-25.850-25.867-25.587
3959821.1721.0921.2621.110.171469111.2990.6121.03217.95732.87016.8043.140-26.569-26.304-23.10224.660-26.259-26.410-26.365
3959921.1621.0621.2021.170.141469110.9490.8910.76717.70630.87717.0902.547-26.652-26.807-22.18824.737-26.783-26.694-26.771
3960021.1921.0521.2221.130.171467110.9980.5630.9118.87928.95716.4413.387-26.545-26.572-22.70524.084-26.618-26.677-26.530
3960121.1521.0421.1721.090.131469111.5561.4181.32812.59832.67116.9492.996-26.106-26.281-22.35924.661-26.134-26.300-26.306
3960221.1721.0821.1921.190.111469111.3821.2151.26310.87429.19416.5823.410-26.486-26.581-22.77224.261-26.491-26.584-26.580
3960321.1621.0921.2121.190.121458111.4820.6061.0838.75929.85915.6593.406-27.308-27.203-24.67423.427-27.250-27.334-27.325
3960421.1721.0921.2221.190.131459111.1171.1540.99313.15924.72016.8233.215-26.502-26.687-22.57724.301-26.388-26.425-26.601
3960521.1521.0521.1621.130.111469110.8950.1870.4779.12326.41215.7574.216-26.760-26.634-24.06623.305-26.536-26.751-26.635
3960621.1721.0821.1821.190.111462111.1470.3480.85210.42130.74516.7813.307-26.054-26.251-23.25724.450-26.224-26.256-26.093